Done Done Course Overview What is AI? What are the Major Challenges? What are the Main Techniques? Where are we failing, and why? Step back and look at the Science Step back and look at the History of AI What are the Major Schools of Thought? What of the Future? Done

Course Overview What are the Main Techniques? (How do we do it?) What is AI? What are the Major Challenges? What are the Main Techniques? (How do we do it?) Where are we failing, and why? Step back and look at the Science Step back and look at the History of AI What are the Major Schools of Thought? What of the Future?

Course Overview What are the Main Techniques? (How do we do it?) What is AI? What are the Major Challenges? What are the Main Techniques? (How do we do it?) Where are we failing, and why? Step back and look at the Science Step back and look at the History of AI What are the Major Schools of Thought? What of the Future? Search Logics (knowledge representation and reasoning) Planning and acting Bayesian belief networks Neural networks Evolutionary computation Reinforcement learning Language parsing and speech techniques Statistical methods (language, learning)

Course Overview What are the Main Techniques? (How do we do it?) What is AI? What are the Major Challenges? What are the Main Techniques? (How do we do it?) Where are we failing, and why? Step back and look at the Science Step back and look at the History of AI What are the Major Schools of Thought? What of the Future? Search Logics (knowledge representation and reasoning) Planning and acting Bayesian belief networks Neural networks Evolutionary computation Reinforcement learning Language parsing and speech techniques Statistical methods (language, learning)

Use “Search” for Pathfinding School Factory Hospital Newsagent church Library Park University Example from Alison Cawsey’s book

Use “Search” for Pathfinding School Factory start Hospital Newsagent church Library Park University finish Example from Alison Cawsey’s book

Use “Search” for Pathfinding library school hospital newsagent park factory university church

Breadth First Search library school hospital newsagent park factory university church

Breadth First Search library school hospital newsagent park factory university church

Breadth First Search library school hospital newsagent park factory university church Put things on the back of the list “to visit later”

Depth First Search library school hospital newsagent park factory university church

Depth First Search library school hospital newsagent park factory university church

Depth First Search library school hospital newsagent park factory university church Put things on the front of the list “to visit later”

Breadth vs. Depth Which is better?

Breadth vs. Depth Which is better? library school university newsagent park factory church stadium grocers hospital market bridge fountain

Breadth vs. Depth Which is better? library school market newsagent park factory church stadium grocers hospital bridge university

Breadth vs. Depth Which is better? Depends on problem Breadth usually needs a lot more memory Remember all the bits you need to expand next Breadth could be good if There are many long dead ends, But one very short successful path Depth could be good if There are many successful paths But all are quite long Can also combine – set a depth limit

What about a big open space? See demo… In Practical 4 Break it up into squares Each node has 8 children (Be careful about looping) That’s an awfully big tree! Need some clever tricks… How would a human do it? Heuristics Search we did before is called “blind” or “brute force” (not clever) Heuristic is a clever rule of thumb

Hill-climbing with Heuristic Heuristic: how close to goal School Factory finish start Hospital Newsagent church Library Park University

Hill-climbing with Heuristic Heuristic: how close to goal Library 9 School 7 Hospital 5 finish Newsagent 0 Park 6 Factory 5 University 3 Church 4

Hill-climbing with Heuristic Heuristic: how close to goal School Factory start Hospital Newsagent church Library Park University finish

Hill-climbing with Heuristic Heuristic: how close to goal Library 9 School 7 Hospital 5 Newsagent 4 Park 2 Factory 5 University 0 Church 4 finish

Hill-climbing with Heuristic Heuristic: how close to goal from Russell and Norvig’s book

Best first Search finish Library 9 School 7 Hospital 5 Newsagent 4 Heuristic: how close to goal Order the list of nodes “to visit later” Do best first But try others later Very good, e.g. in open space Doesn’t consider how far we’ve come though A* - more in practical Library 9 School 7 Hospital 5 Newsagent 4 Park 2 Factory 5 University 0 Church 4 finish

Search is an abstract technique…

Remember: General Problem Solving

Remember: General Problem Solving Problem formulation Initial situation Goal situation Actions that can be done +cost of action Constraints Task: Find the best sequence of permissible actions that can transform the initial situation into the goal situation. 6 1 7 3 4 5 8 2

Search is an abstract technique… Jugs problem Two jugs, 4 litre and 3 litre Want to get 2 litres in 4 litre jug Formulate problem Can represent state as (0,0) or (4,0) or (4,2)… Actions: Fill 4 litre ( _ , _ )  ( 4 , _ ) Fill 3 litre ( _ , _ )  ( _ , 3 ) Empty 4 litre ( _ , _ )  ( 0 , _ ) Empty 3 litre ( _ , _ )  ( _ , 0 ) What else?

Search for games: Minimax

Search for games: Minimax

Alpha-Beta pruning example

Alpha-Beta pruning example Alpha value I get at least this

Alpha-Beta pruning example Alpha value I get at least this Beta value I get at most this (if I go here)

Alpha-Beta pruning example Beta value I get at most this (if I go here)

Alpha-Beta pruning example

Alpha-Beta pruning example

Alpha-Beta pruning example No question about 3 now

What about real (hard) games? So far we searched all the way to the end of the game Not feasible in chess, branching factor 35 So far we didn’t use heuristics Do a limited lookahead Distance to goal? Evaluate the board state Requires intelligence: pieces, their positions, and stage in game How much lookahead? Modern computer? Alpha beta can give about double 4 moves ≈ human novice 8 moves ≈ human master 12 moves ≈ Deep Blue, Kasparov Deep Blue had extra tricks to look further on interesting paths Go Branching factor ≈ 300… forget it! Use databases of patterns Explain combinatorial explosion Exponential growth

What is search good for? Pretty much everything! Pathfinding, puzzles, general problem solver, games Scheduling deliveries Arranging the CS1013 timetable Diagnostic systems find a set of malfunctions that explain the symptoms Speech recognition find the right sequence of words Finding templates/models to match a visual scene Learning is search for a hypothesis Planning systems Find a sequence of actions that achieves a given goal We will look at this next week

Defence A big user of AI. "... the deployment of a single logistics support aid called DART during the Desert Shield/Storm Campaign paid back all US government investment in AI/KBS research over a 30 year period." Tate A. Smart Planning. ARPI Proc. 1996.

Search is an abstract technique… What are we really doing here? What is the science of abstraction? Mathematics Look at problems abstractly See that they’re the same Use one technique for many problems

Note on Heuristics Interesting because human heuristics go awry Hard to come up with a good heuristic Often use human intelligence Is the chess computer smart? What about TD-Backgammon Try the Missionaries and Cannibals http://www.learn4good.com/games/puzzle/boat.htm Interesting because human heuristics go awry Computer is not confused Remember the first law 6 1 7 3 4 5 8 2 1 2 3 4 5 6 7 8

Recap: What have you learned about “Search” “Blind” or “brute force” techniques Breadth first Depth first Heuristic techniques Hill-climbing Best first A* - more in practical General problem solving Game playing Minimax Alpha-Beta pruning Search can apply to many diverse problems Makes some tasks simple for computers Heuristics need some intelligence Musings… Computer: Some simple tricks can go a long way Power of computer to store so much and go so fast Just like life – simple blocks

Course Overview What are the Main Techniques? (How do we do it?) What is AI? What are the Major Challenges? What are the Main Techniques? (How do we do it?) Where are we failing, and why? Step back and look at the Science Step back and look at the History of AI What are the Major Schools of Thought? What of the Future? Search Logics (knowledge representation and reasoning) Planning and acting Bayesian belief networks Neural networks Evolutionary computation Reinforcement learning Language parsing and speech techniques Statistical methods (language, learning)